In CP terminology, the assignment of a variable to a value is called a labelling step. If such an assignment is made, it is tested for consistency with the variable's current domain. If the assignment is consistent, it is accepted, which leads to propagation of new constraints. Alternatively, the assignment may lead to an inconsistency, in which case a failure is triggered and the labelling step is rejected [1]}. A key aspect of the search component in CP is the variable ordering strategy that guides the selection of variables for labeling. Devising an effective variable ordering strategy can lead to substantial gains in terms of identifying high-quality regions of the search space and better quality solutions more quickly. The focus of our study is to achieve an improved variable ordering strategy in CP via supervised learning, and we demonstrate this can be effectively achieved using JSS as a case study.
